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Federated Learning (FL) is a distributed learning framework that can dealwith the distributed issue in machine learning and still guarantee highlearning performance. However, it is impractical that all users will sacrificetheir resources to join the FL algorithm. This motivates us to study theincentive mechanism design for FL. In this paper, we consider a FL system thatinvolves one base station (BS) and multiple mobile users. The mobile users usetheir own data to train the local machine learning model, and then send thetrained models to the BS, which generates the initial model, collects localmodels and constructs the global model. Then, we formulate the incentivemechanism between the BS and mobile users as an auction game where the BS is anauctioneer and the mobile users are the sellers. In the proposed game, eachmobile user submits its bids according to the minimal energy cost that themobile users experiences in participating in FL. To decide winners in theauction and maximize social welfare, we propose the primal-dual greedy auctionmechanism. The proposed mechanism can guarantee three economic properties,namely, truthfulness, individual rationality and efficiency. Finally, numericalresults are shown to demonstrate the performance effectiveness of our proposedmechanism.

An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach